Action Recognition.
HuMMan provides action labels and 3D skeletal positions, which can verify its usefulness on 3D action recognition.
Specifically, we train popular graph-based methods (STGCN [1]} and 2s-AGCN [2]}) on HuMMan. Results are shown in Table  REF . Compared to NTU RGB+D, a large-scale 3D action recognition dataset and a standard benchmark that contains 120 actions [3]}, HuMMan may be more challenging since 2s-AGCN [2]} achieves Top-1 accuracy of 88.9% and 82.9% on NTU RGB+D 60 and 120 respectively, but 74.1% only on HuMMan. The difficulties come from the whole-body coverage design in our action set, instead of over-emphasis on certain body parts (NTU RGB+D has a large proportion of upper body movements). Moreover, we observe a significant gap between Top-1 and Top-5 accuracy (\(\sim \) 30%). We attribute this phenomenon to the fact that there are plenty of intra-actions in HuMMan. For example, there are similar variants of push-ups such as quadruped push-ups, kneeling push-ups, and leg push-ups. This challenges the model to pay more attention to the fine-grained differences in these actions. Hence, we find HuMMan would serve as an indicative benchmark for fine-grained action understanding.
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